Author: Group of Authors
Publisher: MAC Prague consulting
ISBN: 8088085276
Category : Business & Economics
Languages : en
Pages : 274
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Book Description
Multidisciplinary Academic Conference on Education, Teaching and Learning, Czech Republic, Prague (MAC-ETL 2019) Multidisciplinary Academic Conference on Management, Marketing and Economics, Czech Republic, Prague (MAC-MME 2019) Multidisciplinary Academic Conference on Transport, Tourism and Sport Science, Czech Republic, Prague (MAC-TTSS 2019)
Author: Group of Authors
Publisher: MAC Prague consulting
ISBN: 8088085268
Category : Business & Economics
Languages : en
Pages : 108
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Book Description
The 15th Multidisciplinary Academic Conference in Prague 2019, Czech Republic (The 15th MAC in Prague 2019)
Author: group of authors
Publisher: MAC Prague consulting
ISBN: 8088085225
Category : Business & Economics
Languages : en
Pages : 343
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Book Description
Multidisciplinary Academic Conference on Education, Teaching and Learning, Czech Republic, Prague (MAC-ETL 2018) Multidisciplinary Academic Conference on Management, Marketing and Economics, Czech Republic, Prague (MAC-MME 2018) Multidisciplinary Academic Conference on Transport, Tourism and Sport Science, Czech Republic, Prague (MAC-TTSS 2018) Friday - Sunday, December 7 - 9, 2018
Author: group of authors
Publisher: MAC Prague consulting
ISBN: 8088085195
Category : Education
Languages : en
Pages : 330
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Book Description
The conference proceedings - International Academic Conference in Prague 2018 (May)
Author: group of authors
Publisher: MAC Prague consulting
ISBN: 8088085217
Category : Education
Languages : en
Pages : 223
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Book Description
The 13th Multidisciplinary Academic Conference in Prague 2018, Czech Republic (The 13th MAC in Prague 2018)
Author: Giovanni Pau
Publisher: MDPI
ISBN: 3036507280
Category : Technology & Engineering
Languages : en
Pages : 234
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Book Description
The International Symposium on Future ICT (Future-ICT 2019) in conjunction with the 4th International Symposium on Mobile Internet Security (MobiSec 2019) was held on 17–19 October 2019 in Taichung, Taiwan. The symposium provided academic and industry professionals an opportunity to discuss the latest issues and progress in advancing smart applications based on future ICT and its relative security. The symposium aimed to publish high-quality papers strictly related to the various theories and practical applications concerning advanced smart applications, future ICT, and related communications and networks. It was expected that the symposium and its publications would be a trigger for further related research and technology improvements in this field.
Author: Pradeep Kumar Singh
Publisher: Springer Nature
ISBN: 9811607338
Category : Technology & Engineering
Languages : en
Pages : 1027
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Book Description
This book features selected research papers presented at the Second International Conference on Computing, Communications, and Cyber-Security (IC4S 2020), organized in Krishna Engineering College (KEC), Ghaziabad, India, along with Academic Associates; Southern Federal University, Russia; IAC Educational, India; and ITS Mohan Nagar, Ghaziabad, India during 3–4 October 2020. It includes innovative work from researchers, leading innovators, and professionals in the area of communication and network technologies, advanced computing technologies, data analytics and intelligent learning, the latest electrical and electronics trends, and security and privacy issues.
Author: Liqun Fu
Publisher: Springer Nature
ISBN: 3031572963
Category :
Languages : en
Pages : 158
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Book Description
Author: Sandeep Saini
Publisher: CRC Press
ISBN: 1000523810
Category : Technology & Engineering
Languages : en
Pages : 329
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Book Description
Machine learning is a potential solution to resolve bottleneck issues in VLSI via optimizing tasks in the design process. This book aims to provide the latest machine-learning–based methods, algorithms, architectures, and frameworks designed for VLSI design. The focus is on digital, analog, and mixed-signal design techniques, device modeling, physical design, hardware implementation, testability, reconfigurable design, synthesis and verification, and related areas. Chapters include case studies as well as novel research ideas in the given field. Overall, the book provides practical implementations of VLSI design, IC design, and hardware realization using machine learning techniques. Features: Provides the details of state-of-the-art machine learning methods used in VLSI design Discusses hardware implementation and device modeling pertaining to machine learning algorithms Explores machine learning for various VLSI architectures and reconfigurable computing Illustrates the latest techniques for device size and feature optimization Highlights the latest case studies and reviews of the methods used for hardware implementation This book is aimed at researchers, professionals, and graduate students in VLSI, machine learning, electrical and electronic engineering, computer engineering, and hardware systems.
Author: Hugo Jair Escalante
Publisher: Springer
ISBN: 3319981315
Category : Computers
Languages : en
Pages : 305
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Book Description
This book compiles leading research on the development of explainable and interpretable machine learning methods in the context of computer vision and machine learning. Research progress in computer vision and pattern recognition has led to a variety of modeling techniques with almost human-like performance. Although these models have obtained astounding results, they are limited in their explainability and interpretability: what is the rationale behind the decision made? what in the model structure explains its functioning? Hence, while good performance is a critical required characteristic for learning machines, explainability and interpretability capabilities are needed to take learning machines to the next step to include them in decision support systems involving human supervision. This book, written by leading international researchers, addresses key topics of explainability and interpretability, including the following: · Evaluation and Generalization in Interpretable Machine Learning · Explanation Methods in Deep Learning · Learning Functional Causal Models with Generative Neural Networks · Learning Interpreatable Rules for Multi-Label Classification · Structuring Neural Networks for More Explainable Predictions · Generating Post Hoc Rationales of Deep Visual Classification Decisions · Ensembling Visual Explanations · Explainable Deep Driving by Visualizing Causal Attention · Interdisciplinary Perspective on Algorithmic Job Candidate Search · Multimodal Personality Trait Analysis for Explainable Modeling of Job Interview Decisions · Inherent Explainability Pattern Theory-based Video Event Interpretations
